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Sensitivity analysis of a two-dimensional quantitative microbiological risk assessment: keeping variability and
Pieter Busschaert1, Annemie H Geeraerd, Mieke Uyttendaele
1CPMF2—Flemish Cluster Predictive Microbiology in Foods, Division of Chemical and Biochemical Process Technologyand Control (BioTeC), Department of Chemical Engineering, Katholieke Universiteit Leuven. W. de Croylaan 46, B-3001 Leuven, Belgium.
Summary
This study presents a new method for sensitivity analysis in quantitative microbiological risk assessment, crucial for understanding pathogen risks in food. The approach effectively separates variability and uncertainty, improving the accuracy of risk estimations for public health interventions.
Area of Science:
- Food safety
- Microbiology
- Risk assessment
Background:
- Quantitative microbiological risk assessment (QMRA) estimates illness risk from foodborne pathogens and evaluates interventions.
- Variability and uncertainty are inherent in QMRA, necessitating stochastic approaches.
- Sensitivity analysis identifies key input variables influencing QMRA outcomes.
Purpose of the Study:
- To develop and validate a novel procedure for sensitivity analysis in QMRA, specifically addressing the challenge of separating variability and uncertainty.
- To compare the impact of variability, uncertainty, model, and scenario uncertainties on risk estimates.
Main Methods:
- A Monte Carlo simulation-based procedure was developed to analyze the relationship between risk estimates and input variable distributions.
- ANOVA-like models and Sobol sensitivity indices were employed to quantify the impact of different uncertainty sources.
- The methodology was applied to a case study of listeriosis risk from deli meat consumption.
Main Results:
- The proposed procedure successfully distinguished the influence of variability and uncertainty on risk assessment outcomes.
- Key input variables contributing to listeriosis risk from deli meats were identified.
- The method provided a comprehensive assessment of various uncertainty types.
Conclusions:
- The developed sensitivity analysis procedure offers a robust framework for QMRA, enhancing the understanding of pathogen risks in food.
- Separating variability and uncertainty improves the reliability of risk assessments and informs targeted public health strategies.
- This methodology is valuable for refining food safety policies and interventions.
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